Context Tree Compression of Multi-Component Map Images

We consider compression of multi-component map images by context modeling and arithmetic coding. We apply optimized multi-level context tree for modeling the individual binary layers. The context pixels can be located within a search area in the current layer, or in a reference layer that has already been compressed. The binary layers are compressed using an optimized processing sequence that makes maximal utilization of the inter-layer dependencies. The structure of the context tree is static variable depth binary tree, and the context information is stored only in the leaves of the tree. The proposed technique achieves improvement of about 25 % over static 16 pixel context template, and 15 % over similar single-level context tree.